AI Driven Cognitive Profiling in Education


Authors : Deborah T. Joy; Charu Jain; Shalini B. Bajaj; Ekta Soni

Volume/Issue : Volume 10 - 2025, Issue 8 - August

Google Scholar : https://tinyurl.com/4482aykd

Scribd : https://tinyurl.com/mr3ju62w

DOI : https://doi.org/10.38124/ijisrt/25aug272

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Abstract : The human mind, in all its ornate intricacy, resists categorisation, yet in its resistance lies the very key to unlocking personalised education. This paper traverses the intersection of artificial intelligence and neuropsychology, seeking not merely to model learner behaviour, but to decode the symphony of cognition that defines individual learning. It is one thing to teach the average, and it is another to teach the individual. By channelling the predictive elegance of Multilayer Perceptron and the generative mimicry of GANs, the authors attempt to sculpt AI systems that understand the slow, the average, and the fast learner not as datapoints, but as dynamic neurological expressions. The work explores whether such systems, imbued with the heuristics of cognitive style and the resonance of personality typologies like MBTI and ILS, can evolve into neuro- aligned pedagogical agents. Rather than reduce learning to analytics alone, the study embraces the challenge of mapping the brain’s plasticity onto algorithmic adaptability, bridging education with empathy, one layer at a time.

Keywords : Artificial Intelligence in Education, Personalized Learning, Learner Profiling, Neuropsychology, Adaptive Learning Systems, Learning Analytics.

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The human mind, in all its ornate intricacy, resists categorisation, yet in its resistance lies the very key to unlocking personalised education. This paper traverses the intersection of artificial intelligence and neuropsychology, seeking not merely to model learner behaviour, but to decode the symphony of cognition that defines individual learning. It is one thing to teach the average, and it is another to teach the individual. By channelling the predictive elegance of Multilayer Perceptron and the generative mimicry of GANs, the authors attempt to sculpt AI systems that understand the slow, the average, and the fast learner not as datapoints, but as dynamic neurological expressions. The work explores whether such systems, imbued with the heuristics of cognitive style and the resonance of personality typologies like MBTI and ILS, can evolve into neuro- aligned pedagogical agents. Rather than reduce learning to analytics alone, the study embraces the challenge of mapping the brain’s plasticity onto algorithmic adaptability, bridging education with empathy, one layer at a time.

Keywords : Artificial Intelligence in Education, Personalized Learning, Learner Profiling, Neuropsychology, Adaptive Learning Systems, Learning Analytics.

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Paper Submission Last Date
30 - November - 2025

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